1. Field
The present disclosure is directed to a method and apparatus for local adaptive provisioning at a node. More particularly, the present disclosure is directed to adjusting node weighted fair queue settings based on queue depth and projected end to end success.
2. Introduction
Presently, information can be sent from a source to a destination over the Internet by breaking data into packets. These packets often travel through multiple intermediate nodes before being reassembled at the destination where the exact path from source to destination may vary due to the amount of network traffic and other factors. Proper network configuration settings, such as proper provisioning, can be critical to such mission success. The success of settings depends on the congestion level of the network and the mix of traffic offered. Unfortunately, without an adaptive strategy based on local information, networks either run sub-optimally or waste valuable resources sending statistical information or control information.
For example, a major challenge in achieving network Quality of Service (QoS) is balancing the desire to maximize the amount of traffic served on the network with the assurance that necessary quality is provided. Over-provisioning can provide assurance that traffic admitted to the network is served properly. Unfortunately, over-provisioning can block traffic at the edges of the network that could have been successful, which results in a failure to maximize the network traffic delivered on the network that successfully contributes to mission success.
Ideally, the distribution of class latencies across a network will just fit within desired limits. A network loaded at a “Just Right” condition will have each class of traffic provisioned sufficiently for all flows to meet End-to-End (E2E) latency requirements where no class is so over-provisioned to the point of wasting bandwidth.
In an example scenario, a network with near optimally tuned Weighted Fair Queue (WFQ) settings can have each class of traffic meeting its E2E latency specification without exceeding a limit of 1% packets late or lost. If such a scenario is modified so the connectivity range of the nodes is reduced, packets will have a higher hop count to reach the same destinations. This causes E2E delays to increase and creates higher failure rates for Voice, Video, and Command and control traffic, which would then be under provisioned. Unfortunately, the solution to such a problem is not optimal because it limits the amount of traffic allowed onto the network at all times, which essentially blocks traffic at the edges. Similarly, if the scenario is modified only with respect to the percent of offered load, unacceptable E2E failures can result, because prior systems do not provide an adaptive means of provisioning network resources such that the maximum amount of prioritized classes of traffic is successfully served. Thus, there is a need for a method and apparatus for local adaptive provisioning at a node.